Cutter suction dredgers (CSDs) play a very important role in the construction of ports, waterways and navigational channels. Currently, most of CSDs are mainly manipulated by human operators, and a large amount of instrument data needs to be monitored in real time in case of unforeseen accidents. In order to reduce the heavy workload of the operators, we propose a data-driven offline learning approach, named Preprocessing-Prediction-Learning Control (PPLC), for obtaining the optimal control policy of the excavating operation of CSDs. The proposed framework consists of three modules, i.e., a data preprocessing module, a dynamics prediction module realized by a Convolutional Neural Network (CNN), and a deep reinforcement learning based control module. The first module is responsible for filtering out irrelevant variables through correlation analysis and dimensionality reduction of raw data. The second module works as a state transition function that provides the dynamics prediction of the excavating operation of a CSD. To realize the learning control, the third module employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to control the swing speed during the excavating operation. The simulation results show that the proposed framework can provide an effective and reliable solution to the automated excavating control of a CSD.

In urban driving scenarios, it is a key component for autonomous vehicles to generate a smooth, kinodynamically feasible, and collision-free path. We present an optimization-based path planning method for autonomous vehicles navigating in cluttered environment, e.g., roads partially blocked by static or moving obstacles. Our method first computes a collision-free reference line using quadratic programming(QP), and then using the reference line as initial guess to generate a smooth and feasible path by iterative optimization using sequential quadratic programming(SQP). It works within a fractions of a second, thus permitting efficient regeneration.

The massive use of information technology has brought certain security risks to the industrial production process. In recent years, cyber-physical attacks against industrial control systems have occurred frequently. Anomaly detection technology is an essential technical means to ensure the safety of industrial control systems. Considering the shortcomings of traditional methods and to facilitate the timely analysis and location of anomalies, this study proposes a solution based on the deep learning method for industrial traffic anomaly detection and attack classification. We use a convolutional neural network deep learning representation model as the detection model. The original one-dimensional data are mapped using the feature mapping method to make them suitable for model processing. The deep learning method can automatically extract critical features and achieve accurate attack classification. We performed a model evaluation using real network attack data from a supervisory control and data acquisition (SCADA) system. The experimental results showed that the proposed method met the anomaly detection and attack classification needs of a SCADA system. The proposed method also promotes the application of deep learning methods in industrial anomaly detection.

Modbus over TCP/IP is one of the most popular industrial network protocol that are widely used in critical infrastructures. However, vulnerability of Modbus TCP protocol has attracted widely concern in the public. The traditional intrusion detection methods can identify some intrusion behaviors, but there are still some problems. In this paper, we present an innovative approach, SD-IDS (Stereo Depth IDS), which is designed for perform real-time deep inspection for Modbus TCP traffic. SD-IDS algorithm is composed of two parts: rule extraction and deep inspection. The rule extraction module not only analyzes the characteristics of industrial traffic, but also explores the semantic relationship among the key field in the Modbus TCP protocol. The deep inspection module is based on rule-based anomaly intrusion detection. Furthermore, we use the online test to evaluate the performance of our SD-IDS system. Our approach get a low rate of false positive and false negative.

Substantial progresses have been made in building versatile anthropomorphic prosthetic hands in the past two decades using emerging technologies. However the trade-offs between functionality, reliability, affordability, appearance, etc. have not been fully settled. Many existing designs, particularly the commercial prosthetic hands, are underactuated and they can realize various grasps through compliant structures or differential mechanisms. This paper presents the design of an underactuated prosthetic hand with one actuator using a continuum differential mechanism. Structure of the continuum differential mechanism is simple enough to allow all the components, including a battery pack, to be packed into the palm. The design concept, component descriptions, and hand constructions are elaborated. Experimental verifications are presented to demonstrate the efficacy of the proposed design.

Currently Software Define Network (SDN) architecture has become a hot topic. Aiming at the authentication security issues of SDN network architecture, we introduce an authentication protocol based on SDN network architecture without any trusted third party between trusted domains. By applying AVISPA security analysis system of network interaction protocol, we can guarantee protocol security and provide complete safety tests. Our work fill the gap of mutual trust between different trusted domains and provide security foundation for interaction between different trusted domains.